108个具有主动和被动解释的新名词-名词复合词的数据集
Phoebe Chen1, David Poeppel1,2,3, Arianna Zuanazzi1,2
1Department of Psychology, New York University, New York, NY, USA.
Journal of open psychology data
|July 21, 2025
概括
研究人员创建了一个新的英语名词-名词组合 (NNC) 数据集,以研究 agentivity 如何影响解释. 此资源有助于研究语义和语法构成.
科学领域:
- 心理语言学 心理语言学
- 计算语言学 计算语言学
- 认知科学 认知科学
背景情况:
- 名词-名词组合 (NNCs) 在英语中很普遍.
- 了解NNC的语义和语法组成对于自然语言处理和认知科学至关重要.
- 代理性,即名词作为代理人的可能性,影响了NNC的解释.
研究的目的:
- 创建一个新的数据集英语NNCs不同的代理性.
- 从母语使用者那里收集这些NNC的积极和被动解释.
- 测量主动和被动的NNC之间的解释时间差异.
主要方法:
- 数据集的创建涉及组合具有高和低代理性的名词.
- 参与者数据 (解释和反应时间) 通过在线众包 (SONA,Prolific) 收集.
- 对主动与被动NNC的解释时间的统计分析.
主要成果:
- 编制了205个新的英语NNC的数据集.
- 翻译数据从58名母语使用者收集.
- 用68名母语人士的数据测量了108家非国家企业的口译时间差异.
结论:
- 创建的数据集为研究NNC组成提供了有价值的资源.
- 这些发现有助于理解在语义和语法处理中作用的作用.
- 这些数据可以为语言理解和语言生产的模型提供信息.
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